Method and apparatus of processing a skin print image

ABSTRACT

In a method and an arrangement for processing a skin print image, and particularly a fingerprint image, which image exists as a gray-level image, provision is made for the gray-level image to be convolved in the direction of two axes (x, y) by generalized gradient filters (Gx, Gy), for the generalized gradients (Bx, By) obtained in this way to be normalized, for the normalized, generalized gradients (Cx, Cy) each to be convolved with generalized gradient filters (Qx, Qy), and for the sum (D) of the two results (Dx, Dy) of the convolution of the normalized, generalized gradients (Cx, Cy) to be converted to binary form.

The invention relates to a method of processing a skin print image, andparticularly a fingerprint image, which image exists as a gray-levelimage. The invention further relates to an arrangement for processing askin print image of this kind.

When an image is made of a print from the skin, and particularly afingerprint image is made, what is obtained is data that represents animage in the form of a number, of greater or lesser magnitude, ofgray-levels. For a subsequent analysis of the image of the fingerprintaimed at comparing images that have currently been made with storedones, it is necessary for relevant features to be extracted. Theseinclude, for example, the position and number of branchings and endingsof ridges (minutiae). It is generally necessary for a binary image to beproduced for processing of this kind, i.e. an image whose brightnessvalues may assume only two states, e.g. black lines on a whitebackground.

It is an object of the present invention to specify a method ofprocessing a skin print image, which image exists in the form of agray-level image. It is also an object of the present invention tospecify an arrangement for processing an image of this kind of a printfrom the skin.

This object is achieved in accordance with the invention by a method bywhich

-   -   the gray-level image is convolved in the direction of two axes        (x, y) by generalized gradient filters (Gx, Gy),    -   the generalized gradients (Bx, By) obtained in this way are        normalized,    -   the normalized, generalized gradients (Cx, Cy) are each        convolved with generalized gradient filters (Qx, Qy) and    -   the sum (D) of the two results (Dx, Dy) of the convolution of        the normalized, generalized gradients (Cx, Cy) is converted to        binary form.

The object is also achieved in accordance with the invention by anarrangement that is provided with means for performing the designatedsignal-processing steps by the method according to the invention. Thesemeans may in particular take the form of means for digital signalprocessing.

A particular provision that is made in the case of the invention is thatthe generalized gradient filters (Gx, Gy, Qx, Qy) each constitute thesuperimposition of a two-dimensional Gaussian bell curve and a suitablyenlarged gradient filter, the size of each of which is adjusted to suitthe average density of the furrows in the skin print image.

To enable relevant features of the skin print image to be extracted, itis also necessary for only those parts of the image to be processed thatare situated within the region of interest (ROI). To determine these,provision may be made in the method according to the invention for items(L) of length information to be obtained from the generalized gradients(Bx, By), which items (L) of length information are compared with apreset length and, if the preset length is exceeded, the given pixel isdesignated as belonging to the region of interest.

The method according to the invention can be implemented, in a mannerthat is particularly efficient in terms of computing time, RAM spacerequired and program memory space required, as a program.

By means of the invention, irregularities in the original image, such astears, pores, scars, creases, differences in intensity caused by drynessor moisture and/or dirt, can be corrected without any complicated andexpensive filtering, such as filtering by Gabor filters and fast Fouriertransforms, for example. Also, the method can easily be adapted todifferent sensors because the number of parameters used is onlyrelatively small. The region of interest is calculated from intermediateresults with only a low computing burden.

These and other aspects of the invention are apparent from and will beelucidated with reference to the embodiments described hereinafter.

In the drawings:

FIG. 1 shows examples of generalized gradient filters, and

FIG. 2 is a flow chart of an embodiment of a computer program forperforming the method according to the invention.

FIG. 1 is a diagrammatic representation of an example of the generalizedgradient filters Gx (FIG. 1 a) and Gy (FIG. 1 b) of 7×7 pixel size. Incontrast to normal gradient filters (which are, for example, −1, 0, +1for the x axis), the sign is inverted for one half following a Gaussianbell curve that is symmetrical in rotation. The size of the filter Gxneeds to be selected in this case to suit the resolution of the image orthe average spacing of the furrows in the fingerprint. When rotatedthrough 90° in a counterclockwise direction, this gives the generalizedgradient filter Gy in the y direction In the flow chart shown in FIG. 2,the program starts at 1 and following this the convolutions Bx=A*Gx andBy =A*Gy are made in step 2. The convolutions produce the values Bx andBy for all the pixels. Then, at 3, the lengths of the generalizedgradients Bx and By are calculated for each of the pixels. The lengthvalues are buffered at 4.

In step 5 of the program, the generalized gradients are normalized,likewise pixel by pixel, to give results Cx and Cy.

The normalized, generalized gradients Cx and Cy are then convolved at 6with generalized gradient filters Qx and Qy to give gradients Dx and Dy.

At 7, the gradients Dx and Dy are added together pixel by pixel.D=(dk,l) is then divided into overlapping, square tiles of equal size,with each dk,l being situated in exactly the same number e of tiles.Each tile is converted into binary form individually by taking the meanof all the gray levels occurring in the tile as a threshold value b forthe conversion of this tile into binary form. All dk,ls>b are set to 1and all dk,ls<b are set to 0. The tiles that have been converted intobinary form in this way are added together in line with their position,thus giving, at 8, a gray-level image B having a maximum of e+1different gray levels. This gray-level image is converted into binaryform as a whole in step 9 of the program, using a suitable threshold c,to give an image F.

At 10, the stored lengths L are convolved in two dimensions following aGaussian bell curve to give a result M. This latter is assessed at 11 asa globally binarized version R of M. At 12, the image E and the image Rare combined to give a resulting image H, whereupon the program isterminated at 13.

1. A method of processing a skin print image, and particularly afingerprint image, which image exists as a gray-level image,characterized in that the gray-level image is convolved in the directionof two axes (x, y) by generalized gradient filters, the generalizedgradients obtained in this way are normalized, the normalized,generalized gradients are each convolved with generalized gradientfilters and the sum of the two results of the convolution of thenormalized, generalized gradients is converted to binary form.
 2. Amethod as claimed in claim 1, characterized in that the generalizedgradient filters each constitute the superimposition of atwo-dimensional Gaussian bell curve and a suitably enlarged gradientfilter, the size of each of which is adjusted to suit the averagedensity of the furrows in the skin print image.
 3. A method as claimedin claim 1, characterized in that, to enable a region of interest of askin print image to be determined from the generalized gradients, itemsof length information are obtained that are compared with a presetlength and, if the preset length is exceeded, the given pixel isdesignated as belonging to the region of interest.
 4. An arrangement forprocessing a skin print image, and particularly a fingerprint image,which image exists as a gray-level image, characterized by means forconvolving the gray-level image in the direction of two axes (x, y) bygeneralized gradient filters, normalizing the generalized gradientsobtained in this way, convolving each of the normalized, generalizedgradients with generalized gradient filters and converting the sum ofthe two results of the convolution of the normalized, generalizedgradients to binary form.
 5. An arrangement as claimed in claim 4,characterized in that the generalized gradient filters each constitutethe superimposition of a two-dimensional Gaussian bell curve and asuitably enlarged gradient filter, the size of each of which is adjustedto suit the average density of the furrows in the skin print image. 6.An arrangement as claimed in claim 4, characterized by a means forobtaining items of length information from the generalized gradients,for comparing these item of length information with a preset length and,if the preset length is exceeded, for designated the given pixel asbelonging to the region of interest.